from langchain_core.runnables import RunnableConfig from typing_extensions import Annotated, TypedDict from langgraph.checkpoint.memory import InMemorySaver from langgraph.graph import StateGraph from langgraph.runtime import Runtime def reducer(a: list, b: int | None) -> list: if b is not None: return a + [b] return a class State(TypedDict): x: Annotated[list, reducer] class Context(TypedDict): r: float graph = StateGraph(state_schema=State, context_schema=Context) def node(state: State, runtime: Runtime[Context]) -> dict: r = runtime.context.get("r", 1.0) x = state["x"][-1] next_value = x * r * (1 - x) return {"x": next_value} graph.add_node("A", node) graph.set_entry_point("A") graph.set_finish_point("A") compiled = graph.compile() step1 = compiled.invoke({"x": 0.5}, context={"r": 3.0}) # {'x': [0.5, 0.75]}

Overrides Graph<N, S, U, StateGraphNodeSpec<S, U>, ToStateDefinition<C>>.constructor · Defined in libs/langgraph-core/dist/graph/state.d.ts:167

October 26, 2024 - State is one of the core components of LangGraph. A node takes state as input, updates it and pass it to the next node in the graph. So the next node takes the response from the previous node as input, updates it and pass it onto the next one.

Edges: Functions that determine which Node to execute next based on the current state. They can be conditional branches or fixed transitions. By composing Nodes and Edges, you can create complex, looping workflows that evolve the state over time. The real power, though, comes from how LangGraph ...

March 24, 2025 - LangGraph is a powerful framework that allows developers to define workflows as directed graphs. One of its core components is StateGraph, which enables efficient state management and execution flow control.

January 17, 2024 - At it's core, LangGraph exposes a pretty narrow interface on top of LangChain. StateGraph is a class that represents the graph. You initialize this class by passing in a state definition.

August 17, 2024 - State in LangGraph is a way to maintain and track information as an AI system processes data. Think of it as the system’s memory, allowing it to remember and update information as it moves through different stages of a workflow, or graph.

const myNode = (state: typeof StateAnnotation.State) => { return { messages: [new AIMessage("Some new response")], sentiment: "positive", }; }; const graph = graphBuilder .addNode("myNode", myNode) .addEdge("__start__", "myNode") .addEdge("myNode", "__end__") .compile(); await graph.invoke({ messages: [new HumanMessage("how are you?")] }); // { // messages: [HumanMessage("how are you?"), AIMessage("Some new response")], // sentiment: "positive", // } ... addSequence→ StateGraph<SD, S, U, N | K, I, O, C, MergeReturnType<NodeReturnType, { [key in string]: NodeOutput }>>method

# Initialize the StateGraph with this state graph = StateGraph(AgentState) # Create nodes and edges ...

3 days ago - Trusted by companies shaping the future of agents – including Klarna, Replit, Elastic, and more – LangGraph is a low-level orchestration framework for building, managing, and deploying long-running, stateful agents.

September 9, 2025 - When the LangGraph graph is built, the state object is associated with the graph: # Build graph graph_builder = StateGraph(GraphState) graph_builder.add_node(nodes...

November 15, 2024 - This is where LangGraph’s core object—the state graph—comes in to help. ... Now that you’ve built the notice parsing and escalation check chains, you need to orchestrate them and add additional functionality that your company requires to process notice emails. To do this, you’ll use LangGraph’s StateGraph to create a graph that builds upon NOTICE_PARSER_CHAIN and ESCALATION_CHECK_CHAIN.

June 26, 2024 - Define a StateGraph object to structure the chatbot as a state machine. The State is a class object defined with a single key messages of type List and uses the add_messages() function to append new messages rather than overwrite them. from typing import Annotated from typing_extensions import ...

November 24, 2024 - For example, a node can integrate with a large language model, process information, call an external API, or any other task. A LangGraph node takes the state of the graph as a parameter and returns an updated state after it is executed.